潜在类模型
潜变量
潜变量模型
班级(哲学)
计量经济学
结构方程建模
混合模型
前因(行为心理学)
变量(数学)
人口
计算机科学
机器学习
心理学
统计
人工智能
数学
社会心理学
人口学
社会学
数学分析
作者
Karen Nylund‐Gibson,Ryan P. Grimm,Katherine E. Masyn
标识
DOI:10.1080/10705511.2019.1590146
摘要
Including auxiliary variables such as antecedent and consequent variables in mixture models provides valuable insight in understanding the population heterogeneity embodied by a latent class variable. The model building process regarding how to include predictors/correlates and outcomes of the latent class variables into mixture models is an area of active research. As such, new methods of including these variables continue to emerge and best practices for the application of these methods in real data settings (including simple guidelines for choosing amongst them) are still not well established. This paper focuses on one type of auxiliary variable—distal outcomes—providing an overview of the methods currently available for estimating the effects of latent class membership on subsequent distal outcomes. We illustrate the recommended methods in the software packages Mplus and Latent Gold using a latent class model to capture population heterogeneity in students’ mathematics attitudes, linking latent class membership to two distal outcomes.
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